智能光网络物理层调制模式识别技术的研究
发布时间：2018-10-15 12:21
【摘要】：不像无线通信领域,调制模式识别技术起步早,发展相对成熟,在光通信领域,特别是智能光网络(Automatic Switch Optical Network,ASON)的调制模式识别技术才刚刚发展。但是对光调制模式的识别,由于其潜在的研究价值和巨大的应用前景,近些年来引起了越来越多的关注。为了从理论上更好的分析智能光网络物理层光传输系统,在本文的研究中采用ASON+DWDM组网方案,提出了一种基于密集波分复用(Dense Wavelength Division Multiplexing,DWDM)的非补偿光传输系统(Uncompensation Transfer,UT)模型,并对该模型的推导进行详细讨论。本文模式识别算法的研究就是基于该DWDM-UT系统模型展开的。研究中共涉及到四类共18种调制信号的判决,它们分别是强度调制信号MASK,相位调制信号MPSK,幅相调制信号MQAM和MAPSK。文中所提出的调制模式识别算法(Modulation Format Identification,MFI)主要基于调制信号高阶累积量(High Order Cumulants,HOC)的特征值参数,利用所设计的分类决策算法对不同的调制信号进行分类判决。对分类判决算法中所使用的阈值,考虑到存在一些调制信号特征值参数随信噪比变化的特性,提出了一种实时训练序列阈值优化(Real-time Training Sequence Threshold Optimization,RT-TSTO)算法,通过对比测试,发现该算法能够有效的保证阈值的精确性,极大地改善了不同调制信号的识别效果。考虑到智能光网络物理层DWDM-UT系统受色散(D)、非线性效应(γ)和传输距离(L)的影响,我们分别研究了这些因素的改变对模式识别性能的影响,并利用仿真工具Matlab给出了相应的仿真结果。与此同时,为了进一步验证本文所提出的MFI算法的有效性,我们对当前已经广泛投入商用的基于PM-QPSK调制的高速率Nyquist WDM也进行了讨论,通过VPI和Matlab的联合仿真,结果表明该系统即使经过超长距离的传输,PM-QPSK调制信号的识别率也能达到96.5%以上。以上仿真结果对今后智能光网络物理链路的工程实施提供了一定的理论依据。
[Abstract]:Unlike in wireless communication, modulation pattern recognition technology starts early and develops relatively mature. Modulation pattern recognition technology in optical communication field, especially in intelligent optical network (Automatic Switch Optical Network,ASON), has just been developed. However, the recognition of optical modulation pattern has attracted more and more attention in recent years because of its potential research value and great application prospect. In order to analyze the physical layer optical transmission system of intelligent optical network better in theory, an uncompensated optical transmission system (Uncompensation Transfer,UT) model based on dense wavelength division multiplexing (Dense Wavelength Division Multiplexing,DWDM) is proposed by using ASON DWDM networking scheme in this paper. The derivation of the model is discussed in detail. The research of pattern recognition algorithm in this paper is based on the DWDM-UT system model. In the study, there are four types of 18 kinds of modulation signals, namely, intensity modulation signal, MASK, phase modulation signal, MPSK, amplitude-phase modulation signal, MQAM and MAPSK., respectively. The proposed modulation pattern recognition algorithm (Modulation Format Identification,MFI) is mainly based on the eigenvalue parameters of the higher-order cumulant (High Order Cumulants,HOC) of the modulation signal. The proposed classification decision algorithm is used to classify different modulated signals. For the threshold used in classifying decision algorithm, considering the fact that there are some characteristic parameters of modulation signal changing with signal-to-noise ratio, a real-time training sequence threshold optimization (Real-time Training Sequence Threshold Optimization,RT-TSTO) algorithm is proposed. It is found that the algorithm can effectively guarantee the accuracy of the threshold and greatly improve the recognition effect of different modulation signals. Considering that the physical layer DWDM-UT system of the intelligent optical network is affected by the dispersion (D), nonlinear effect (纬) and the transmission distance (L), we study the effect of these factors on the pattern recognition performance. The corresponding simulation results are given by using the simulation tool Matlab. At the same time, in order to further verify the effectiveness of the proposed MFI algorithm, we also discuss the high rate Nyquist WDM based on PM-QPSK modulation, which has been widely used in commercial applications, and through the joint simulation of VPI and Matlab. The results show that the recognition rate of PM-QPSK modulation signal can reach more than 96.5% even though the system is transmitted over a long distance. The above simulation results provide a theoretical basis for the implementation of the physical link of the intelligent optical network in the future.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN929.1
本文编号：2272539
[Abstract]:Unlike in wireless communication, modulation pattern recognition technology starts early and develops relatively mature. Modulation pattern recognition technology in optical communication field, especially in intelligent optical network (Automatic Switch Optical Network,ASON), has just been developed. However, the recognition of optical modulation pattern has attracted more and more attention in recent years because of its potential research value and great application prospect. In order to analyze the physical layer optical transmission system of intelligent optical network better in theory, an uncompensated optical transmission system (Uncompensation Transfer,UT) model based on dense wavelength division multiplexing (Dense Wavelength Division Multiplexing,DWDM) is proposed by using ASON DWDM networking scheme in this paper. The derivation of the model is discussed in detail. The research of pattern recognition algorithm in this paper is based on the DWDM-UT system model. In the study, there are four types of 18 kinds of modulation signals, namely, intensity modulation signal, MASK, phase modulation signal, MPSK, amplitude-phase modulation signal, MQAM and MAPSK., respectively. The proposed modulation pattern recognition algorithm (Modulation Format Identification,MFI) is mainly based on the eigenvalue parameters of the higher-order cumulant (High Order Cumulants,HOC) of the modulation signal. The proposed classification decision algorithm is used to classify different modulated signals. For the threshold used in classifying decision algorithm, considering the fact that there are some characteristic parameters of modulation signal changing with signal-to-noise ratio, a real-time training sequence threshold optimization (Real-time Training Sequence Threshold Optimization,RT-TSTO) algorithm is proposed. It is found that the algorithm can effectively guarantee the accuracy of the threshold and greatly improve the recognition effect of different modulation signals. Considering that the physical layer DWDM-UT system of the intelligent optical network is affected by the dispersion (D), nonlinear effect (纬) and the transmission distance (L), we study the effect of these factors on the pattern recognition performance. The corresponding simulation results are given by using the simulation tool Matlab. At the same time, in order to further verify the effectiveness of the proposed MFI algorithm, we also discuss the high rate Nyquist WDM based on PM-QPSK modulation, which has been widely used in commercial applications, and through the joint simulation of VPI and Matlab. The results show that the recognition rate of PM-QPSK modulation signal can reach more than 96.5% even though the system is transmitted over a long distance. The above simulation results provide a theoretical basis for the implementation of the physical link of the intelligent optical network in the future.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN929.1
【引证文献】
相关期刊论文 前1条
1 余庚;;基于约束的ASON生存性探讨[J];光通信技术;2018年01期
，本文编号：2272539
本文链接：https://www.wllwen.com/kejilunwen/xinxigongchenglunwen/2272539.html
